During the propagation of a radioelectric signal, arising from the phase, frequency and/or amplitude modulation of a carrier by a string of symbols, the signal is subjected to several sources of degradations.
Among these sources of degradations is the noise intrinsic to the receiver, which can be modelled as Gaussian White Noise. When the signal is received on several sensors (or antennas), the noise is assumed to be white spatially (independence between the sensors) and temporally (independence in time) and the noise correlation matrix R can be written R=diag(σk2), with σk2 the noise power received on sensor k.
Among these sources of degradations are also the paths related to the multiple reflections of the electromagnetic wave during its propagation. These multi-paths are due to the reflections on the diverse elements of the environment, such as buildings or the terrestrial relief, but may also originate, as is often the case when dealing with HF transmissions, from reflections on the various ionospheric layers of the atmosphere. These reflections generate replicas of the signal that are shifted in time, in phase and/or in amplitude. When the multi-paths are received in a time interval that is less than the duration of a symbol, they may sum together in a constructive or destructive manner. One then speaks of flat fading, which, in order to improve the quality of reception, requires the implementation of techniques making it possible to impart diversity to the signal:
temporal diversity in case of mobility, obtained by way of an interleaving and of a coding of the data over a more significant duration than the coherence time of the propagation pathway,
frequency diversity obtained by way of a frequency hopping and data coding mechanism, or
spatial diversity obtained through the use of a plurality of judiciously spaced transmit and/or receive antennas.
When the multi-paths are received in a time interval greater than the duration of a symbol, they generate inter-symbol interferences which degrade the quality of the radio link: one then speaks of selective fading. To remedy this problem, it is necessary to resort to pathway coding techniques, making it possible to improve the robustness of the signal, to equalization techniques, seeking to estimate the propagation pathway and to recombine in time and in phase the various paths, or to antenna processing techniques, aimed at optimizing the reception of the various useful paths by recombining in an effective manner the signals arising from the various receive antennas.
Finally, among these sources of degradations are the interferences related to jamming, be it intentional (broadband deliberate jamming for example) or unintentional (other signals transmitted in the same frequency resource). Processings specific to the fight against jamming are then necessary, such as error-correcting coding, the excision of jammers by notch filtering, frequency evasion or else antenna processing techniques which are aimed at recombining the signals received on an array of sensors to eliminate the influence of the interferences while optimizing the reception of the useful signal.
Among the set of techniques making it possible to combat the various sources of degradations affecting the reception of a useful signal, antenna processing techniques, or multichannel processings, are the most promising, since they make it possible, by processing the signals received on the various antennas of an array, to optimize the reception of the useful signal in the presence of flat fading, of selective fading and of interference.
Single-channel or SISO processings (the acronym standing for Single Input Single Output), such as pathway coding or equalization, exhibit their limits once the level of the interference becomes too significant or the propagation conditions are too complex (flat fading whilst the coherence band of the pathway is higher than the frequency hopping band, selective fading, the spreading of whose paths is too significant to be able to be equalized, broadband interferences, etc.). It is then necessary to combine them with multichannel processings, the exploitation of the spatial diversity afforded by the use of several antennas making it possible to profit from the difference between the transmission pathways relating to each antenna.
Among the multichannel processings are distinguished the processings for which the antenna diversity is afforded on transmission, called MISO processings (the acronym standing for Multiple Input Single Output), the processings for which the antenna diversity is afforded on reception, called SIMO processings (the acronym standing for Single Input Multiple Output), and the processings for which the antenna diversity is afforded at one and the same time on transmission and on reception, called MIMO processings (the acronym standing for Multiple Input Multiple Output).
The invention relates to SIMO processings.
In the absence of interference, various SIMO antenna processings can be envisaged as a function of the characteristics of the useful propagation pathway.
When the fading is “flat” (absence of intersymbols interference), the optimal SIMO processing is the so-called MRC processing (the acronym standing for Maximal Ratio-Combining). In an MRC multichannel receiving station, the signals arising from the various reception channels are recombined in phase and in amplitude, in such a way as to maximize the signal-to-noise ratio (SNR) of the useful signal. Thus, in the presence of spatially and temporally white noise (that is to say in the absence of interference), and for flat fading (absence of intersymbols interference), MRC processing allows optimal exploitation of the spatial diversity.
When the fading is “selective” in frequency (presence of intersymbols interference), the MRC receiver sees its performance deteriorate and the implementation of a multichannel equalization technique is necessary. Several multichannel equalization solutions have been proposed in the literature.
Some of these equalization solutions are termed “non anti-jammed”, that is to say that they are designed to operate in the absence of jammers. The noise present on each of the channels is therefore temporally white, and uncorrelated between the channels (therefore spatially white).
Among these techniques, the optimal solution in the maximum likelihood sense is the receiver based on the Spatio-Temporal Matched Filter in Spatially and Temporally White Noise (STMF-STWN) followed by a decision unit based on a Viterbi algorithm. STMF-STWN consists in performing an estimation of the propagation pathway and then a matched filtering adapted to the propagation pathway on each of the reception channels, and then in summing the signals obtained at the output of the matched filters. On output from STMF-STWN, the signal-to-noise ratio is maximized on the current symbol and the residual inter-symbol interference is processed by a Viterbi algorithm. This receiver is optimal in the absence of interference, whether the fading is selective or whether it is flat.
However, the STMF-STWN performance is no longer satisfactory once the signal received contains interference which is not narrow-band interference. The implementation of other types of SIMO processings is then necessary.
To allow operation in the presence of interference, that is to say in a jammed environment for which the noise received on the various channels is no longer spatially white, so-called “anti-jammed” multichannel equalization schemes are known. These schemes integrate techniques specially dedicated to the fight against the interference related to jamming and noise that is not spatially white. They are also known by the name of antenna filtering techniques (under this rubric, an antenna is composed of several elementary antennas and the antenna filtering is aimed at recombining the signals arising from these various elementary antennas so as to optimize the reception of the useful signal in the presence of interference), or else of adaptive antenna techniques (to underline the fact that the processings are capable of adapting automatically to alterations in the conditions of propagation and of interference).
These techniques have been developed starting from the 1960s. Initially, they were based on spatial filtering (therefore without any notion of equalization) of the signals received, that is to say with an amplitude/phase weighting on each sensor. Then, starting from the 1980s/90s, spatio-temporal structures, allowing genuine multichannel equalization of the signals received, were proposed, so as to follow the evolution of the waveforms and the increase in the bandwidths of the modulations.
Thus, the simplest structure making it possible to combat interferences is a spatial structure whose complex weights on each of the sensors are adapted through a criterion for minimizing a Mean Square Error (MSE) between the antenna output signal and a replica signal. Such an antenna, dubbed SMFR (Spatial Matched Filter adapted with the aid of a Replica), allows the rejection of jammers, but in the presence of useful propagation multipaths:
it “points” in the direction of one of the paths (the one which is correlated with the replica), that is to say that it resets in phase the contributions of this path on the various sensors. When the antenna is composed of omnidirectional sensors, the expected gain in signal-to-noise ratio is of the order of 10 log K, where K is the number of sensors used,
it seeks to reject the paths that are decorrelated from the path towards which it points (thus losing the energy associated with these paths), these being seen by the antenna as entirely separate jammers.
Such an antenna may therefore be heavily penalized in the presence of several useful propagation paths. Indeed, the rejection of the decorrelated useful paths may occur to the detriment of the rejection of the jammers, the performance of the multisensor receiver may even become worse than that of the single-sensor receiver when two temporally decorrelated propagation paths are very correlated spatially.
In order to improve the performance of the latter antenna processing technique, the idea is to couple it with a single-sensor equalization technique. One thus obtains multisensor equalizers comprising a spatial part, composed of various filters disposed on each of the reception channels, and a temporal part disposed at the output of the spatial part.
It is possible to cite in this regard European patent EP 0867079 B1, which seeks to carry out the spatial filtering of the signals received on the array of sensors, while optimizing a spatio-temporal criterion making it possible to preserve the whole set of useful paths. This filtering must therefore be followed by a step of equalizing a single-channel signal. The role of the spatial filtering is to recombine the signals received on the various channels, while rejecting the interference, if any, as well as the reflected paths whose delay is greater than the maximum delay corrected by the single-channel equalizer which follows it.
Among the single-channel equalization techniques usable at the output of the spatial filtering, the optimal solution in the maximum likelihood sense is based on a matched filtering adapted to the propagation pathway, implemented after a pathway estimation step, followed by a decision unit based on a Viterbi algorithm. On output from the pathway-matched filter, the signal-to-noise ratio is maximized on the current symbol (the various paths are recombined on the current symbol, in phase) and the residual inter-symbol interference is processed by a Viterbi algorithm.
The main drawback of this single-channel equalization technique stems from the fact that the Viterbi algorithm requires a computational power that grows as ML, with M the order of the constellation and L the length of the estimated propagation pathway (measured in symbol times). Thus, this receiver can be envisaged on waveforms such as GSM (the acronym standing for Global System for Mobile communications), for which M=2 and L=5, but not on more recent waveforms, for which the modulation band is more significant and where the constellations are of higher order.
An alternative to the Viterbi algorithm, which is less expensive in terms of computational power, consists in implementing a single-channel equalizer at the output of the spatial filtering. The single-channel equalizer can be any type of equalizer known to the person skilled in the art, such as for example a DFE equalizer (the acronym standing for Decision Feedback Equalizer), BDFE equalizer (the acronym standing for Block-DFE), an FDE (Frequency Domain Equalization) equalizer, a transverse equalizer, or a turbo equalizer. These equalizers can be based on so-called ZF criteria (the acronym standing for Zero-Forcing), MMSE criteria (the acronym standing for Minimum Mean Square Error), MLSE criteria (the acronym standing for Maximum Likelihood Sequence Estimator), or other criteria.
In patent EP 0867079 B1, to carry out the spatial filtering of the multichannel signal received, a vector w, composed of one coefficient per channel, is used during a step of antenna processing and of recombining of the channels defining a temporal filter applied to the training sequence. This vector w is computed, jointly with a vector v in such a way as to minimize the mean square error between the output signal of the spatial part, corresponding to the signal filtered by w, and the output signal of the temporal part, corresponding to the training sequence filtered by v. The vector v is a computation intermediary making it possible to compute the vector w.
Two constraints are envisaged in computing the vectors w and v. The first constraint is a norm constraint which makes it possible to optimize the spatial filter when the equalizer placed at output is an equalizer based on the Viterbi algorithm (the computed spatial filter optimizes the global signal-to-noise over interference ratio (or SNIR, standing for Signal over Noise plus Interference Ratio) associated with all the paths), while the second constraint is a pointing constraint which makes it possible to optimize the spatial filter when the equalizer placed at output is of the MMSE type (the computed spatial filter optimizes the SNIR associated with the main path so as to favour the operation of the equalizer placed at output).
Patent EP 0867079 B1 exhibits a certain number of drawbacks, however. The main ones are the following:                the implementation of the antenna processing requires the inversion of matrices, an operation which is very expensive in terms of computation time. Some of these matrices are computed on the basis of known training sequences, and can be precomputed upstream, and then stored in memory, correspondingly decreasing the required computational power. However, the size of these matrices is significant and their storage may pose a problem as the number of matrices to be stored increases. Such is the case in particular in applications aimed at decreasing the signature of the waveform by using a large number of different training sequences.        the algorithms described make it necessary to take a temporal reference i0, chosen as corresponding to the path of strongest power, determined during an upstream synchronization phase. This criterion therefore considers only a single one of the paths, and is not necessarily the optimal choice criterion.        the performance related to the spatial filtering may be improved, by modifying the way in which the coefficients of this filtering are computed so as not to reject the paths situated in the window of the single-channel filtering. Moreover, the spatial filtering can be afforded an additional temporal dimension so as to allow it to attenuate multi-paths situated outside of the window of the equalizer and/or to place in phase multiple paths situated in the horizon of the equalizer, and to correct mispairings between the channels of the receiver.        